MWLN: Multilevel Wavelet Learning Network for Continuous-Scale Remote-Sensing Image Super-Resolution

Abstract

Remote-sensing image super-resolution (SR) reconstructs high resolution (HR) with texture from the input low resolution (LR). It has been widely used and applied in image-processing tasks. However, most algorithms focus on designing more complex structures to enhance performance, ignoring learning frequency information. Moreover, existing methods are designed for SR tasks with specific scales, such as scales of 2 and 4. It limits the network performance in applications. To alleviate the above issues, this letter designs a multilevel wavelet learning network (MWLN) for continuous-scale remote-sensing image SR. MWLN achieves continuous magnification remote-sensing image SR tasks without training at different scales multiple times through multilevel wavelet feature aggregation (MWFA) and self-learning implicit representation (SLIR). MWFA extracts hierarchical features and applies discrete wavelet transforms (DWTs), capturing high-frequency information while avoiding information loss. Moreover, this letter cascades a multidimensional attention mechanism model channel and spatial features and enhances features' interaction. SLIR maps the image coordinates and red, green, and blue (RGB) value through self-learning, realizing the continuous-scale reconstruction. Extensive experimental results demonstrate that MWLN outperforms the compared methods in quantitative and qualitative results on specific and continuous-scale remote-sensing image SR tasks.